Job
Description
About this opportunity: This position plays a crucial role in the development of Python-based solutions, their deployment within a Kubernetes-based environment, and ensuring the smooth data flow for our machine learning and data science initiatives. The ideal candidate will possess a strong foundation in Python programming, hands-on experience with ElasticSearch, Logstash, and Kibana (ELK), a solid grasp of fundamental Spark concepts, and familiarity with visualization tools such as Grafana and Kibana. Furthermore, a background in ML Ops and expertise in both machine learning model development and deployment will be highly advantageous. What you will do: Python Development: Write clean, efficient, and maintainable Python code to support data engineering tasks, including data collection, transformation, and integration with machine learning models. Data Pipeline Development: Design, develop, and maintain robust data pipelines that efficiently gather, process, and transform data from various sources into a format suitable for machine learning and data science tasks using ELK stack, Python and other leading technologies. Spark Knowledge: Apply basic Spark concepts for distributed data processing when necessary, optimizing data workflows for performance and scalability. ELK Integration: Utilize ElasticSearch, Logstash, and Kibana (ELK) for data management, data indexing, and real-time data visualization. Knowledge of OpenSearch and related stack would be beneficial. Grafana and Kibana: Create and manage dashboards and visualizations using Grafana and Kibana to provide real-time insights into data and system performance. Kubernetes Deployment: Deploy data engineering solutions and machine learning models to a Kubernetes-based environment, ensuring security, scalability, reliability, and high availability. What you will Bring: Machine Learning Model Development: Collaborate with data scientists to develop and implement machine learning models, ensuring they meet performance and accuracy requirements. Model Deployment and Monitoring: Deploy machine learning models and implement monitoring solutions to track model performance, drift, and health. Data Quality and Governance: Implement data quality checks and data governance practices to ensure data accuracy, consistency, and compliance with data privacy regulations. MLOps (Added Advantage): Contribute to the implementation of MLOps practices, including model deployment, monitoring, and automation of machine learning workflows. Documentation: Maintain clear and comprehensive documentation for data engineering processes, ELK configurations, machine learning models, visualizations, and deployments. Why join Ericsson? What happens once you apply? Primary country and city: India (IN) || Bangalore Req ID: 766745
